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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Do technological innovations affect unemployment? Some empirical evidence from European countries. Economies, 5(4), 48. https://doi.org/10.3390/economies5040048
. (2017). Emotional processes in human-robot interaction during brief cognitive testing. Computers In Human Behavior, 90, 331 - 342. https://doi.org/10.1016/j.chb.2018.08.013
. (2019). Ethics of using Artificial Intelligence to augment drafting legal documents. Texas A&M Journal Of Property Law, 4(5). Retrieved de https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=1080&context=journal-of-property-law
. (2018). . (2018).
Exploring the tension between transparency and datification effects of open government IS through the lens of Complex Adaptive Systems. The Journal Of Strategic Information Systems, 26(3), 210 - 232. https://doi.org/10.1016/j.jsis.2017.07.001
. (2017). foo.castr: visualising the future AI workforce. Big Data Analytics, 3(1). https://doi.org/10.1186/s41044-018-0034-z
. (2018). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46 - 60. https://doi.org/10.1016/j.futures.2017.03.006
. (2017). Four fundamentals of workplace automation. Mckinsey Quarterly, 1–9.
. (2015). Four fundamentals of workplace automation. Mckinsey Quarterly, 1–9.
. (2015). The future digital work force: Robotic process automation (RPA). Journal Of Information Systems And Technology Management, 16. https://doi.org/10.4301/S1807-1775201916001
. (2019). The future of health care: Protocol for measuring the potential of task automation grounded in the national health service primary care system. Jmir Research Protocols, 8(4), e11232. https://doi.org/10.2196/11232
. (2019). High tech, low growth: Robots and the future of work abstract. Historical Materialism, 26(4), 3 - 34. https://doi.org/10.1163/1569206X-00001745
. (2018). The history of technological anxiety and the future of economic growth: Is this time different?. Journal Of Economic Perspectives, 29(3), 31 - 50. https://doi.org/10.1257/jep.29.3.31
. (2015). How do machine learning, robotic process automation, and blockchains affect the human factor in business process management?. Communications Of The Association For Information Systems, 297 - 320. https://doi.org/10.17705/1CAIS.04319
. (2018).